ACE: Ant Colony Based Multi-Level Network Embedding for Hierarchical Graph Representation Learning

Abstract

As a popularly used technique for feature learning in graphs, network embedding aims to represent each node as a low-dimensional vector to support efficient graph analytic tasks, such as node classification, link prediction, and visualization. The key to this representation method is that the embedding vector of a node should preserve its properties in the graph as much as possible. Most traditional network embedding algorithms only consider the local neighborhood as the context to build the node representation and fail to capture the important hierarchical clustering property ubiquitous in real-world graphs. To solve this problem, we propose ACE, a novel network embedding method, to preserve the features of hierarchical clustering structures. ACE works by using an ant colony-based graph coarsening algorithm to group the nodes according to their relationship to achieve a multi-level clustering pyramid of the input graph. Then, we generate the embedding vectors from multiple layers of the graph pyramid and blend these multi-level vectors into the final representation of nodes based on the PCA dimension reduction algorithm. We demonstrate the effectiveness of ACE over state-of-the-art network embedding algorithms on the node classification tasks in several real-world graph datasets. The experiments show that ACE is easy to be integrated with other network embedding algorithms, such as DeepWalk, Line, node2vec, and SDNE, to significantly improve their performance by up to 22% on Macro F1. The source code and the datasets used in this paper are available on Github ( https://github.com/so-link/ACE_Embedding ).